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Related Experiment Video

Updated: Apr 4, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Evaluating geodesic active contours in microcalcifications segmentation on mammograms.

Marcelo A Duarte1, Andre V Alvarenga2, Carolina M Azevedo3

  • 1Biomedical Engineering Program, Instituto Alberto Luiz Coimbra (COPPE), Federal University of Rio de Janeiro, Rio de Janeiro 21941-972, Brazil.

Computer Methods and Programs in Biomedicine
|September 14, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for segmenting microcalcifications in mammograms, improving early breast cancer detection. Radiologists confirmed its accuracy, achieving at least 86.9% for diagnostic segmentation.

Keywords:
Breast cancerGeodesic active contoursMammographyMicrocalcificationRadiologists’ knowledgeSegmentation

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Oncology

Background:

  • Breast cancer is a leading cause of cancer death in women, with incidence rising globally.
  • Early detection through mammography is crucial for reducing mortality but faces limitations in accuracy.
  • Computer-aided diagnosis (CADx) systems aim to enhance mammographic interpretation, with segmentation being a critical first step.

Purpose of the Study:

  • To develop and evaluate an improved microcalcification segmentation method for mammographic analysis.
  • To integrate radiologists' expertise into the segmentation process for more accurate lesion identification.
  • To enhance the accuracy of computer-aided diagnosis (CADx) systems for breast cancer screening.

Main Methods:

  • A novel microcalcification segmentation technique combining geodesic active contours (GAC) with anisotropic texture filtering.
  • Incorporation of radiologist input for final segmentation selection within regions of interest (ROIs).
  • Validation using 1000 ROIs from the Digital Database for Screening Mammography (DDSM).

Main Results:

  • The method achieved an adequate segmentation rate of at least 86.9% for diagnostic purposes, as confirmed by radiologists.
  • Quantitative evaluation using the area overlap measure (AOM) yielded a mean of 0.52±0.20.
  • The method demonstrated comparable or superior performance to existing literature for microcalcifications larger than 460μm, with limitations for smaller ones.

Conclusions:

  • The proposed microcalcification segmentation method, incorporating radiologist feedback, shows significant promise for improving mammographic analysis.
  • Accurate segmentation is vital for subsequent steps in CADx systems, directly impacting diagnostic accuracy.
  • Further refinement may be needed to optimize performance for smaller microcalcifications in breast cancer screening.